The field of the invention is systems and methods for medical image quality enhancement. More particularly, the invention relates to systems and methods for enhancing medical images using a multiscale decomposition framework.
Medical images contain image structures of varying scales, with different scales representing different components. For example, in cardiac images, left ventricle, myocardium, and blood pool are large-scale structures, whereas infarct and noise are represented by relatively small-scale structures. Thus, extracting different scales in an image (i.e., multiscale image representation) is a valuable tool in medical image processing.
There are various multiscale representation techniques based on different image decomposition algorithms and denoising methods. Gaussian blurring with varying standard deviation can be considered as a multiscale representation, but it diffuses the image isotropically, thereby diffusing main edges. On the other hand, inverse scale representations based on variational formulations preserve edges, but tend to be time consuming and thus unsuitable for real-time applications.
Noise present in medical images makes it difficult for clinicians to use these images for diagnosis. It also makes it problematic in many image processing algorithms, including image registration. Thus, denoising is used as a preprocessing step before most medical image processing techniques. Nevertheless, edges present in medical images are very important and must not be diffused during the denoising step. For instance, in denoising magnetic resonance images, it is important to preserve edges. Gaussian filtering, being isotropic in nature, smoothes all edges, hence anisotropic filters such as the bilateral filter are commonly used in medical imaging applications.
It would therefore be desirable to provide a method for denoising medical images that not only preserves edge features in the original image, but also achieves denoising in less time than currently available techniques so as to provide for real-time denoising applications.